Prediction of media success from plot summaries using machine learning model

ABSTRACT

Disclosed is a machine learning-based media success prediction through plot summaries According to an embodiment, a method comprises performing preprocessing on text data including a plot summary, calculating a sentiment score from the preprocessed text data using a first model, generating first input data using the calculated sentiment score, generating second input data from the preprocessed data using a second model, and determining a candidate class of content corresponding to the plot summary by applying the first input data and the second input data to a pre-trained third model. The candidate class includes a first class indicating success and a second class indicating failure.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. 119 toKorean Patent Application No. 10-2019-0179963, filed on Dec. 31, 2019,in the Korean Intellectual Property Office, the disclosure of which isherein incorporated by reference in its entirety.

TECHNICAL FIELD

The disclosure relates to machine learning-based media successprediction through plot summary.

DESCRIPTION OF RELATED ART

Artificial intelligence technology consists of machine learning (deeplearning) and element techniques using machine learning.

Machine learning is an algorithm technique that it itself may classifyand learn the features of input data. The component technology is atechnique for mimicking the human brain's perception and decisioncapabilities using a machine learning algorithm (e.g., deep learning),and this may be divided into several technical fields, such aslinguistic understanding, visual understanding, inference/prediction,knowledge expression, and operation control.

In the field of linguistic understanding, some attempts have been madeto predict, via machine learning, the success or failure of media,including a movie or TV show, before the media is released to generalconsumers using only the plot summary of the media. However, methods ofpredicting the success of a movie using spoilers suffer from poorperformance and low reliability due to insufficient training data andtest data. Although there are other attempts that use both factors thatmay be gathered after the move is released, as well as movie plotsummary, they are unable to predict whether media will succeed or failfor the purpose of decision-making on media production or investment.

SUMMARY

The disclosure aims to address the foregoing issues and/or needs.

The disclosure aims to implement a machine learning-based media successprediction through plot summary, which may predict the success rate ofmedia using only the plot summary of the media.

According to an embodiment, a method comprises performing preprocessingon text data including a plot summary, calculating a sentiment scorefrom the preprocessed text data using a first model, generating firstinput data using the calculated sentiment score, generating second inputdata from the preprocessed data using a second model, and determining acandidate class of content corresponding to the plot summary by applyingthe first input data and the second input data to a pre-trained thirdmodel. The candidate class includes a first class indicating success anda second class indicating failure.

The candidate class may include a first class indicating success and asecond class indicating failure.

The plot summary may include at least one of a movie, a musical, aconcert, a play, a sports game, an exhibition, a book or music.

Performing the preprocessing may dividing the text data into sentences.

Performing the preprocessing may include generating a list of thesentences of the text data.

The sentiment score may include a positive score, a negative score, aneutral score, or a compound score.

The first model may be a neural network model trained by providinginformation for the sentiment score and the preprocessed text data, astraining data.

The first model may be a valence aware dictionary for sentimentreasoning (VADER) sentiment analyzer.

The sentiment score may be an N-dimensional vector. Calculating thesentiment score may include calculating the sentiment score for each ofa plurality of sentences constituting the plot summary, in reverse orderfrom a last sentence among the plurality of sentences. When a number ofthe sentences is M which is less than N, zero-padding may be applied toas many remaining dimensions as N−M.

The second model is an embeddings from language models (ELMO) model.

Generating the first input data may include generating a first featurevector by applying the sentiment score to a merged one-dimensionalconvolutional neural networks (1D CNN).

Generating the first input data may include generating a first vector byapplying the sentiment score to a first bidirectional long short-termmemory (LSTM), generating a second vector by applying the sentimentscore to a second bidirectional LSTM, and generating a second featurevector by adding the first vector and the second vector.

Determining the candidate class may include generating a concatenatedvector by concatenating the first input data and the second input data,and determining the candidate class of the content corresponding to theplot summary by applying the concatenated vector to the pre-trainedthird model.

The third model may be a classification model pre-trained based on aplot summary for training, labeled with a success score for the content.The plot summary for training may be the preprocessed text data.

The success score for the content may be an evaluation score of aconsumer for the plot summary for training. The evaluation score being Xor more may be classified as the first class indicating success, and theevaluation score being less than Y may be classified as the secondclass.

According to an embodiment, X may be different from Y.

The machine learning-based media success prediction through plotsummary, according to an embodiment of the disclosure provides thefollowing effects.

In the disclosure, it is possible to predict the success rate of mediausing only the plot summary of the media.

Effects of the disclosure are not limited to the foregoing, and otherunmentioned effects would be apparent to one of ordinary skill in theart from the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the disclosure and many of the attendantaspects thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawings, wherein:

FIG. 1 is a block diagram illustrating a success prediction deviceaccording to an embodiment of the disclosure;

FIG. 2 is a block diagram illustrating a success prediction processaccording to an embodiment of the disclosure;

FIG. 3 is a view illustrating an example process of extracting asentiment score according to an embodiment of the disclosure;

FIG. 4A is a view illustrating a process of generating a feature vectorusing merged 1D-CNN;

FIG. 4B is a view illustrating a process of generating a feature vectorusing residual bidirectional LSTM;

FIG. 5 is a view illustrating 1D CNN; and

FIG. 6 is a flowchart illustrating a success prediction method accordingto an embodiment of the disclosure.

DETAILED DESCRIPTION

Hereinafter, exemplary embodiments of the disclosure are described indetail with reference to the accompanying drawings. The same referencedenotations may be used to refer to the same or similar elementsthroughout the specification and the drawings, and no duplicatedescription is given. As used herein, the terms “module” and “unit” areprovided solely for ease of description and these terms may be usedinterchangeably but rather than being distinct in meaning or role. Whendetermined to make the subject matter of the disclosure unclear, thedetailed description of the known art or functions may be skipped. Theaccompanying drawings are provided merely for a better understanding ofthe disclosure and the technical spirit or the scope of the disclosureare not limited by the drawings.

The terms coming with ordinal numbers such as ‘first’ and ‘second’ maybe used to denote various components, but the components are not limitedby the terms. The terms are used to distinguish one component fromanother.

It will be understood that when an element or layer is referred to asbeing “on,” “connected to,” “coupled to,” or “adjacent to” anotherelement or layer, it may be directly on, connected, coupled, or adjacentto the other element or layer, or intervening elements or layers may bepresent. In contrast, when a component is “directly connected to” or“directly coupled to” another component, no other intervening componentsmay intervene therebetween.

As used herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise.

It will be further understood that the terms “comprise” and/or “have,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

<Success Prediction Device>

FIG. 1 is a block diagram illustrating a success prediction deviceaccording to an embodiment of the disclosure.

Referring to FIG. 1 , a success prediction device 100 may include atleast one processor 110, a memory 120, and a communication module 130.

The processor 110 may include one or more application processors (APs),one or more communication processors (CPs), or at least one or moreartificial intelligence (AI) processors. The application processor,communication processor, or AI processor 110 may be separately includedin different integrated circuit (IC) packages or may be included in oneIC package.

The application processor may control multiple hardware and softwarecomponents connected to the application processor by driving anoperating system or application programs, and the application processormay process or compute various data including multimedia data. Forexample, the application processor may be implemented as a system onchip (SoC). The processor 110 may further include a graphic processingunit (GPU) (not shown).

The communication processor may manage a data link and convert acommunication protocol in communication between the success predictiondevice 100 and other electronic devices connected to the successprediction device 100 through a network. As an example, thecommunication processor may be implemented as an SoC. The communicationprocessor may perform at least some of multimedia control functions.

The communication processor may control data transmission/reception ofthe communication module 130. The communication processor may beimplemented to be included as at least a part of the applicationprocessor.

The application processor or the communication processor may loadcommands or data received from at least one of a nonvolatile memory 120or other components connected thereto to the volatile memory 120 andprocess the same. The application processor or the communicationprocessor may store, in the non-volatile memory 120, data received fromat least one of the other components or data generated by at least oneof the other components.

The processor 110 (in particular, an AI processor) may learn a neuralnetwork using a program stored in the memory 120. The processor 110 maylearn a neural network for recognizing data related to operations of thesuccess prediction device 100. Here, the neural network may be designedto simulate a human brain structure (e.g., the neuron structure of thehuman neural network) on a computer. The neural network may include aninput layer, an output layer, and at least one hidden layer. Each layerincludes at least one neuron having a weight, and the neural network mayinclude synapses connecting the neurons. In the neural network, eachneuron may output an input signal input through the synapse, as afunction value of an activation function for weight and/or bias.

A plurality of network nodes may send and receive data according totheir respective connection relationships so as to simulate the synapticactivity of neurons that send and receive signals through synapses. In adeep learning model, a plurality of network nodes may be located indifferent layers and exchange data according to a convolutionalconnection relationship. Examples of neural network models include adeep neural network (DNN), a convolutional neural network (CNN), arecurrent neural network, a restricted Boltzmann machine, a deep beliefnetwork, a deep Q-Network, or such various deep learning schemes and maybe applied in fields such as vision recognition, speech recognition,natural language processing, and voice/signal processing.

The processor 110 performing the above-described functions may be ageneral-purpose processor (e.g., CPU) or may be an AI-dedicatedprocessor (e.g., GPU) for artificial intelligence learning.

In various embodiments of the disclosure, the processor 110 may extracta sentiment score based on text data composed of plot summaries ofvarious contents and determine whether or not the content is successfulusing only the plot summaries using the extracted sentiment score andvarious neural network models. A detailed description is made below withreference to FIG. 2 and its subsequent figures.

The memory 120 may include an internal memory or external memory. Theinternal memory may include, e.g., a volatile memory (e.g., a dynamicRAM (DRAM), a static RAM (SRAM), a synchronous dynamic RAM (SDRAM),etc.) or a non-volatile memory (e.g., a one time programmable ROM(OTPROM), a programmable ROM (PROM), an erasable and programmable ROM(EPROM), an electrically erasable and programmable ROM (EEPROM), a maskROM, a flash ROM, a NAND flash memory, or a NOR flash memory). Accordingto an embodiment, the internal memory may take the form of a solid statedrive (SSD). The external memory may include a flash drive, e.g., a CF(compact flash) memory, an SD (secure digital) memory, a micro-SDmemory, a mini-SD memory, an xD (extreme digital) memory, or a MemoryStick™.

The memory 120 of the box-office prediction device 100 according to anembodiment of the disclosure may store a learning corpus composed of aplurality of sentences. The learning corpus may include text datacomposed of various languages and/or accents. The learning corpus may betext data gathered through a sensor (not shown) or a camera (not shown)of the success prediction device 100 or received from a communicableexternal terminal using the communication module 130. The memory 120 maystore a learning model generated through a learning algorithm forclassification/recognition of data according to an embodiment of thedisclosure. Furthermore, the memory 120 may store input data of alearning model, training data, or a learning history.

The communication module 130 may include a wireless communication moduleor an RF module. The wireless communication module may include, e.g.,Wi-Fi®, Bluetooth® (BT), global positioning system (GPS) or near fieldcommunication (NFC). For example, the wireless communication module mayprovide a wireless communication function using a radio frequency.Additionally or alternatively, the wireless communication module mayinclude a network interface or modem for connecting the successprediction device 100 with a network (e.g., Internet, local area network(LAN), wide area network (WAN), telecommunication network, cellularnetwork, satellite network, plain old telephone service (POTS) or 5Gnetwork).

The RF module may be responsible for data transmission/reception, e.g.,transmitting and receiving data RF signals or invoked electronicsignals. As an example, the RF module may include, e.g., a transceiver,a PAM (power amp module), a frequency filter, or an LNA (low noiseamplifier) (not shown). The RF module may further include parts (e.g.,conductors or wires) for communicating radio waves in a free space uponperforming wireless communication.

The success prediction device 100 according to various embodiments ofthe disclosure may be implemented as at least one of a server, TV,refrigerator, oven, clothes styler, vacuum robot, drone, airconditioner, air purifier, PC, speaker, home security camera, lighting,washing machine, and smart plug. Since the components of the successprediction device 100 described in connection with FIG. 1 are examplecomponents typically provided in electronic devices, the successprediction device 100 according to the embodiment of the disclosure isnot limited by the above-described components and, as necessary, mayomit or add components.

FIG. 2 is a block diagram illustrating a success prediction processaccording to an embodiment of the disclosure.

Referring to FIG. 2 , the processor 110 of the success prediction device100 may collect training data in order to generate a deep learning ormachine learning-based neural network model for success prediction. Inthis case, the training data used for training the neural network modelmay be pre-processed in a predetermined form in order to implement thelearning efficiency and/or high performance of the neural network model.In an embodiment, the processor 110 may extract, through thepreprocessing module 220, text data composed of plot summaries ofcontent on a per-sentence basis. The content is one or more performancesor works that may have a plot summary, and includes, but is not limitedto, at least one of movies, musicals, concerts, plays, sports games,exhibitions, books, and music. FIG. 2 illustrates an example in whichthe content is a movie, but various embodiments or scope of thedisclosure is not limited thereto or thereby.

In an embodiment of the disclosure, the processor 110 may create a listof the data extracted in units of sentences through the preprocessingmodule 220. The so-generated sentence list may include at least onesentence in the form of a list. The sentence list may be used as inputdata when a context vector or a sentiment score is generated orcalculated later.

The processor 110 of the success prediction device 100 may apply thepreprocessed data to a first model 241 or a second model 231. Here, thefirst model 241 may be defined as a sentiment score extraction module,and the second model 231 may be defined as an ELMO vectorization module.

The first model 241 may be a neural network model trained by providinginformation for sentiment scores and preprocessed data, as trainingdata. The sentimental scores may include positive scores, negativescores, neutral scores, or compound scores. The compound score mayrepresent the sum of the sentiment scores for all normalized vocabularybetween −1 (the maximum negative score) and +1 (the maximum positivescore). As an example, the first model 241 may be implemented as a VADERsentiment analyzer. The sentiment score generated using the first model241 may be expressed as an N-dimensional vector. For example, the Ndimension may be 198 dimensions. This is because in the case of a movie,the longest plot summary has 198 sentences. N may be varied tocorrespond to the number of sentences of the longest plot summary ofeach content based on the type of content. In one embodiment, thesentiment score for each of the plurality of sentences constituting theplot summary may be calculated in reverse order from the last sentenceamong the plurality of sentences and, when the number (=M) of thesentences is less than N, zero-padding may be applied to as manyremaining dimensions (or sentences) as (N−M). Referring to FIG. 3 , theprocessor 110 may obtain a positive score 322 of 0.056, a negative score323 of 0.275, and a neutral score 324 of 0.669 for the sentence “Lokiescapes after killing Coulson and ejecting Thor from the airship, whilethe Hulk falls to the ground after attacking a SHIELD” (310), using thefirst model 241, and may apply their respective weights to the scores,obtaining a compound score 321 of −0.7845. The sentence may be a Oth (Ois a natural number) sentence from the last sentence. When the plotsummary input to the first model 241 includes P sentences (P is anatural number less than N), P+1, . . . , N-dimensional vector spaceshave O's as zero padding is applied. This does not affect theperformance of the success prediction process. The reason for processingthe sentiment factor as shown in FIG. 3 is that the number of sentencesincluded in each of the plurality of plot summary differs and, in lightof the general plot summary configuration, the highlight is highlylikely to be positioned at the end.

The processor 110 may generate an embedding vector from the preprocesseddata (e.g., the plurality of sentences constituting the plot summary orthe sentence list extracted from the plurality of sentences) using thesecond model 231. In one embodiment, the second model 231 may beimplemented as an ELMO, as described above. The embedding method byWord2vec and Glove is used in various types of natural languageprocessing (NLP), but the embedding method by Word2vec and Glove cannotdeal with words having different meanings depending on the context, suchas homophones. Thus, according to an embodiment, the second model 231uses an ELMO embedding method that generates different embedding vectorsaccording to contexts.

The processor 110 may apply the sentiment score generated using thefirst model 241 to merged 1D-CNN or residual bidirectional LSTM. Theprocessor 110 may apply the embedding vector generated using the secondmodel 231 to the FC layer 232. The processor 110 may generate aconcatenated vector by concatenating the feature vector generated merged1D-CNN or residual bidirectional LSTM 242 and the feature vectorgenerated from the ELMO model and the FC layer. The so-generatedconcatenated vector may be applied as an input to a pre-trained thirdmodel 252. The third model 252 is a neural network-based classificationmodel and is a classification model that has been supervised-trainedbased on the pre-processed plot summary for training and a success scorefor the content of the plot summary for training.

FIG. 4A is a view illustrating a process of generating a feature vectorusing merged 1D-CNN, and FIG. 4B is a view illustrating a process ofgenerating a feature vector using residual bidirectional LSTM.

Referring to FIG. 4(a), the processor 110 may input preprocessed text301 a to an ELMO and may input a vector representing a sentiment score302 a to a merged 1D-CNN. ELMO embedding enters a vector of 1024dimensions as mean-pooling applies to the sentence vector of each movieplot summary and may be a vector of 256 dimensions through afully-connected layer (FC). The sentiment vector representing thesentiment score is calculated through the 1D CNN layer, as shown in FIG.5 , and a convolution operation of (198−3+1) is performed through a64-dimensional feature detector and a 3-size kernel. As a result ofperforming the convolution operation, a vector of (Z X 196×64)dimensions is formed (Z is the number of samples). The following 1D CNNis also computed under the same process, forming a (Z X196×64)-dimensional vector. The so-formed vector forms a vector with themain characteristics reduced in half in the max pooling layer, and thedimensions are expanded through the flatten layer to be combined withthe result of ELMO embedding. As an example, as a result of expandingthe dimensions, the sentiment vector may be expressed as a featurevector having 100 dimensions. The processor 110 generates an input valueof the classification model by combining the 256-dimensional featurevector extracted from the above-described preprocessed text and the100-dimensional feature vector extracted from the sentiment score anddetermines whether the content succeeds (1) and/or fails (0) with theclassification model generated according to the generated input value.

In the process of generating a feature vector using the residualbidirectional LSTM of FIG. 4(b), ELMO embedding is the same as that ofFIG. 4(a) using 1D CNN. In the case of FIG. 4(b), the dependence of along sentence may be mitigated by using two bidirectional LSTMs.Specifically, the processor 110 may summate the output values of thefirst bidirectional LSTM and the second bidirectional LSTM and flattenthe result of the summation. In this case, the input of the secondbidirectional LSTM may be an output of the first bidirectional LSTM. Theprocessor 110 may combine the feature vector extracted from thepreprocessed text 301 b and the feature vector based on the sentimentscore 302 b, and determine whether the content succeeds (1) and/or fails(0).

The success prediction device according to various embodiments of thedisclosure may use a binary cross-entropy error as a loss function inorder to binary-classify the result value as 1 or 0.

FIG. 6 is a flowchart illustrating a success prediction method accordingto an embodiment of the disclosure.

Referring to FIG. 6 , the processor 110 of the success prediction device100 may perform pre-processing on the text including the plot summary(S110). The plot summary includes a plot summary about at least one of amovie, musical, concert, play, sports game, exhibition, book or music.The preprocessing process may include either a method of dividing thetext data composed of plot summaries into sentence units or a method ofdividing the text data composed of plot summaries into sentence unitsand generating a list including a plurality of sentences from thedivided sentence units.

The processor 110 may input the preprocessed data into a first model(e.g., VADER sentiment analyzer) or a second model (e.g., ELMO) (S120).Specifically, the processor 110 may calculate a sentiment score (e.g., apositive score, a negative score, a neutral score, or a compound score)from the preprocessed data using the first model. The first model may bea neural network model pre-trained by providing information forsentiment scores and preprocessed data, as training data. In variousembodiments of the disclosure, the sentiment score may be expressed asan N-dimensional sentiment vector. The sentiment vector is generated bycalculating the sentiment score for each of the plurality of sentencescontinuously from the last sentence among the plurality of sentencesconstituting the plot summary. When the number (=M) of the plurality ofsentences is less than N, the processor 110 may apply zero padding to asmany remaining dimensions as (N−M) of the dimensions of the sentimentvector.

The processor 110 may generate first input data using the calculatedsentiment score. Specifically, in various embodiments of the disclosure,the processor 110 may input an output of a sentiment analyzer to 1D-CNNor residual bidirectional LSTM (S130), and the first input data may begenerated as an output of the 1D-CNN or residual bidirectional LSTM. Theprocessor 110 may generate second input data from the preprocessed data,using the second model. The first input data and the second input datamay be generated in the form of a vector and may then be applied asinput data to the classification model.

The processor 110 may combine the output of the ELMO embedding layer andthe output of 1D-CNN or residual bidirectional LSTM and apply the sameto the classification model and, based on the result, determine whetherthe content succeeds or fails (S140). Specifically, the processor 110may determine a candidate class of the content corresponding to the plotsummary by applying the first input data and the second input data to apre-trained third model. The candidate class may include a first classindicating success and a second class indicating failure. Morespecifically, the processor 110 may generate a concatenated vector bycombining the first input data and the second input data, and appliesthe generated concatenated vector to a pre-learned classification model,determining the candidate class of the content corresponding to the plotsummary. In this case, the classification model is a classificationmodel that is pre-trained based on plot summaries for training, labeledwith success scores for the content, and the plot summaries for trainingmay be composed of pre-processed text data. The success score for thecontent is the evaluation score of the consumer for plot summary fortraining, and if the evaluation score is X or more, it may be classifiedas the first class indicating success, and if it is less than Y, it maybe classified as the second class indicating failure. In this case, Xand Y may have different values.

The above-described embodiments of the disclosure may be implemented incode that a computer may read out of a recording medium. Thecomputer-readable recording medium includes all types of recordingdevices storing data readable by a computer system. Examples of thecomputer-readable recording medium include hard disk drives (HDDs),solid state disks (SSDs), silicon disk drives (SDDs), read-only memories(ROMs), random access memories (RAMs), CD-ROMs, magnetic tapes, floppydisks, or optical data storage devices, or carrier wave-typeimplementations (e.g., transmissions over the Internet). Thus, the abovedescription should be interpreted not as limiting in all aspects but asexemplary. The scope of the disclosure should be determined byreasonable interpretations of the appended claims and all equivalents ofthe disclosure belong to the scope of the disclosure.

What is claimed is:
 1. A processor-implemented method, comprising:performing preprocessing on text data and a success score, wherein thetext data only includes a plot summary about a story of a content, andthe success score corresponds to the content; calculating a sentimentscore for each of a plurality of sentences constituting the plot summaryfrom the preprocessed text data using a first model, wherein thesentiment score which is an N-dimensional vector is calculated inreverse order from a last sentence among the plurality of sentences, andwhen a number of the sentences M is less than N, zero-padding is appliedto as many remaining dimensions as N−M; generating first input data asan output of a merged one-dimensional convolution neural network (1DCNN) or residual bidirectional long short-term memory (LSTM) by applyingthe calculated sentiment score to the merged 1D CNN or residualbidirectional LSTM; generating second input data from the preprocessedtext data using a second model including an embeddings from languagemodels (ELMO) model; and determining a candidate class of contentcorresponding to the plot summary by combining the first input data as afirst output from the merged 1D CNN or residual bidirectional LSTM andthe second input data as a second output from the ELMO embedding layer,and applying the first input data and the second input data to apre-trained third model, wherein the third model is a classificationmodel pre-trained based on only a training plot summary about a story ofa content, labeled with the success score corresponding to the content,and the training plot summary is the preprocessed text data, wherein thecandidate class includes a first class indicating success and a secondclass indicating failure, and wherein the success score is an evaluationscore previously evaluated by a consumer for the content.
 2. Theprocessor-implemented method of claim 1, wherein the plot summaryincludes at least one of a movie, a musical, a concert, a play, a sportsgame, an exhibition, a book or music.
 3. The processor-implementedmethod of claim 1, wherein performing the preprocessing includesdividing the text data into sentences.
 4. The processor-implementedmethod of claim 3, wherein performing the preprocessing includesgenerating a list of the sentences of the text data.
 5. Theprocessor-implemented method of claim 1, wherein the sentiment scoreincludes a positive score, a negative score, a neutral score, or acompound score.
 6. The processor-implemented method of claim 1, whereinthe first model is a neural network model trained by providinginformation for the sentiment score and the preprocessed text data, astraining data.
 7. The process-implemented method of claim 1, wherein thefirst model is a valence aware dictionary and sentiment reasoner (VADER)sentiment analyzer.
 8. The processor-implemented method of claim 1,wherein generating the first input data includes generating a firstfeature vector by applying the sentiment score to the mergedone-dimensional convolutional neural network (1D CNN).
 9. Theprocessor-implemented method of claim 1, wherein: generating the firstinput data includes: generating a first vector by applying the sentimentscore to a first bidirectional long short term memory (LSTM), generatinga second vector by applying the sentiment score to a secondbidirectional LSTM; and generating a second feature vector by adding thefirst vector and the second vector.
 10. The processor-implemented methodof claim 1, wherein: determining the candidate class includes:generating a concatenated vector by concatenating the first input dataand the second input data; and determining the candidate class of thecontent corresponding to the plot summary by applying the concatenatedvector to the pre-trained third model.
 11. The processor-implementedmethod of claim 1, wherein the evaluation score being X or more isclassified as the first class, and the evaluation score being less thanY is classified as the second class.
 12. The processor-implementedmethod of claim 11, wherein X is different from Y.
 13. A non-transitorycomputer system-readable recording medium recording a program forexecuting the method of claim 1, on a computer system.